Efficient recursive kernel canonical variate analysis for monitoring nonlinear time‐varying processes
Bibliographic record
Abstract
Abstract Kernel canonical variate analysis (KCVA) cannot be adopted for monitoring nonlinear time‐varying processes because of changes in variance, mean, and correlation between variables. Efficient recursive kernel canonical variate analysis (ERKCVA) is thus proposed to monitor the nonlinear time‐varying processes. In a high‐dimensional feature space, the covariance matrix can be updated recursively by the exponentially weighted moving average approach. The first‐order perturbation theory is introduced to obtain the recursive singular value decomposition of the Hankel matrix, which can significantly reduce the computational cost of the proposed method. Prediction errors and state variables are non‐Gaussian; thus, upper control limits can be derived from the estimated probability density function by kernel density estimation. The proposed method is demonstrated by simulating a continuous stirred tank reactor. Simulation results indicate that ERKCVA could efficiently capture the predefined normal and natural changes in nonlinear time‐varying processes. In addition, ERKCVA can also identify 4 types of sensor faults.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".